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arXiv · 2502.21057

Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control

Taeho Lee·Donghwan Lee
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Abstract

This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player zero-sum dynamic game between a user and an adversary. The method combines deterministic policy gradients with deep reinforcement learning to train a robust policy that attenuates disturbances efficiently. A practical variant, robust deep deterministic policy gradient (RDDPG), integrates twin-delayed updates for stability and sample efficiency. Experiments on an unmanned aerial vehicle demonstrate superior robustness and tracking accuracy under severe disturbance conditions.

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BibTeXRIS

Taeho Lee, Donghwan Lee. 2025-02-28. Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control. https://arxiv.org/abs/2502.21057

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